2. Marine Protected Areas
Old Paradigm:
Protect areas where we know there is high biodiversity
Protect areas with endemic species
NOW we know
Protecting these areas is important BUT not enough to protect the ecosystems AND
We have imperfect information about these anyway

3. Marine Protected Areas
New Paradigm:
Ecologically representative network of marine protected areas
Convention on Biological Diversity (CBD) Aichi Target 11 is 10% of marine areas
Includes examples of all habitat types
We don’t have complete information about biodiversity in the marine environment so how do we choose “ecologically representative” (versus special, unique) areas to protect?

4. Solution: use bioregions
Defined as: “a region defined by characteristics of the natural environment rather than by man-made divisions.”
It is a value-neutral way to describe the entire marine environment.
Bioregions can be described using comprehensive layers of environmental data: surrogates for imperfect biological information.
Every part of the marine environment belongs to one bioregion or another.
No bioregion is more important than any other.

5. What are bioregions? Areas of relative similarity
Habitats, communities, and physical features within a bioregion are more similar to each other than those in a different bioregion.
A way to represent the full range of biodiversity
A hierarchical or nested classification of habitat types (provinces, biomes, habitat complexes, etc.)

6. Bioregions as a planning tool
If the objective is an ecologically representative network of marine protected areas covering 10% of the marine environment with the goal of enhancing biodiversity (CBD Aichi Target 11)
Then a protected area target of 10% for each bioregion will help meet that goal

7. Bioregions as a planning tool
If one objective is an ecologically representative network of marine protected areas covering 10% of the marine environment with the goal of enhancing biodiversity (CBD Aichi Target 11) Then a protected area target of 10% for each bioregion will help meet that objective

8. Biogeographical Provinces
Global bioregions
Biogeographical Provinces
GOODS
LME
.MEOW / PPOW
EMU

9. Longhurst, 2010. Biogeographical Provinces
4 global provinces: 3 in Oceania
52 sub-provinces: 9 in Oceania
MACBIO

10. Longhurst, 2010. Biogeographical Provinces
Factors: Based on biophysical proxies: phytoplankton abundance (chlorophyll concentration), mixed layer depth, currents, clarity.
Method: Expert-driven approach
MACBIO

11. Spalding, et al., 2012. Marine Ecoregions and Pelagic Provinces of the World (MEOW PPOW)
62 MEOW ecoregions: 21 in Oceania
7 PPOW provinces: 3 in Oceania
MACBIO

12. Spalding, et al., 2012. Marine Ecoregions and Pelagic Provinces of the World (MEOW PPOW)
Factors: Based on taxonomically homogeneous areas
Method: expert-driven approach
MACBIO

13. Watling, et al., 2013. Global Open Ocean and Deep Sea (GOODS) Classification
14 abyssal provinces: 4 in Oceania
14 bathyal provinces: 5 in Oceania
MACBIO

14. Watling, et al., 2013. Global Open Ocean and Deep Sea (GOODS) Classification
Factors: depth, temperature, salinity, dissolved oxygen, particulate organic carbon flux
Method: expert-driven approach
MACBIO

15. NOAA. 2013 Large Marine Ecosystems (LME)
66 LMEs: 0 in Oceania
MACBIO

16. NOAA. 2013. Large Marine Ecosystems (LME)
Factors: bathymetry, hydrograpy, productivity, trophic relationships
Method: expert-driven approach
MACBIO

17. Wright, et al., 2016 Ecological Marine Units (EMU)
37 ecological marine units at 9 depth levels: 3 in Oceania
MACBIO

18. Wright, et al., 2016 Ecological Marine Units (EMU)
Factors: Depth, Temperature, Salinity, Dissolved Oxygen, Nitrate, Phosphate, Silicate.
Method: k-means clustering algorithm
MACBIO

19. Bioregions as a planning tool
The MACBIO project is working with 5 countries to conduct Marine Spatial Planning within their EEZs. Global-scale bioregions are not useful for national scale marine planning and management.

20. MACBIO Deep Water Bioregions
400 Deep Water Bioregions in Oceania
MACBIO

21. MACBIO Deep Water Bioregions
Factors: ocean chemistry: calcite, oxygen, nitrate, solar irradiance, pH, phosphate, salinity, silicate, chlorophyll. physical characteristics: depth, 20°C isotherm, mixed layer depth, temperature at surface, 30m, 200m, 1000m, dynamic height of sea surface, distance from land.
Methods: k-means clustering, hierarchical clustering
MACBIO

22. MACBIO Reef-Associated Bioregions
Factors: 1,405 fish species surveyed at 3,846 sites. Modelled 435 fish species occurrence probability for all inshore areas using chemical and physical characteristics: calcite, chlorophyll, and nitrate concentration, solar irradiance, pH and sea surface temperature.
Methods: hierarchical clustering
MACBIO

23. MACBIO Reef-Associated Bioregions
Factors: 1,405 fish species surveyed at 3,846 sites. Modelled 435 fish species occurrence probability for all inshore areas using chemical and physical characteristics: calcite, chlorophyll, and nitrate concentration, solar irradiance, pH and sea surface temperature.
Methods: hierarchical clustering
MACBIO

24. Clustering Algorithms
k-means: a vector quantisation algorithm that iteratively partitions observations into clusters with the nearest mean.

25. Clustering Algorithms
Hierarchical Clustering: a divisive hierarchy of clusters; all observations start in one cluster and splits are done recursively.

26. Next Steps Deep Water Bioregions Reef Associated Bioregions
Expert input to adjust boundaries
Finalise bioregions
Reef Associated Bioregions
Integration of both sets of bioregions

27. Questions?
Jonah Sullivan Marine and Coastal Biodiversity Management in Pacific Island Countries (MACBIO) Senior GIS Officer The International Union for Conservation of Nature (IUCN)